df2 <- df %>% mutate(artists = str_remove_all(artists, "\\['"),
                     artists = str_remove_all(artists, "\\']"),
                     artists = gsub(",", " and ", artists),
                     artists = str_remove_all(artists, "^'|'$"), # work on this a bit more
                     decade = as.factor(floor(year/10)*10), # make decade column
                     year = as.factor(year),
                     release_date = ifelse(nchar(release_date) == 4, paste0(release_date, "-01-01"), release_date)) # Make release_date a real date column
# Also would be cool to try to recognize gender by name and make a dummy column

df_genre2 <- df_genre %>% mutate(genres = str_remove_all(genres, "\\["), # Get rid of brackets
                     genres = str_remove_all(genres, "\\]"),
                     genres = str_split(genres, ",")) %>%  # Split up genres by comma
                     unnest(genres) %>% 
                     mutate(genres = str_remove_all(genres, "\\ '")) %>% # remove all space appostrophes
                            mutate(genres = str_remove_all(genres, "\\'")) %>% #remove all appostrophes, could be a better filter than this
                     mutate(genres = str_remove_all(genres, "\"")) # Some quotes stuck around on childrens music category
# df_genre3 <- df_genre2 %>% pivot_wider(names_from = artists, values_from = genres)

df_str(df, return = "plot")

df %>% freqs(popularity, plot = T)

df %>% freqs(year, plot = T)

df %>% mutate(popularity = round(popularity/10)) %>% freqs(popularity, year, plot = T, results = F, top = 50)

df %>% 
  mutate(explicit = as.factor(explicit),
         popularity = round(popularity/10)) %>%
  freqs(popularity, explicit, plot = T,
        title = "Popularity by Explicitness",
        subtitle = paste("Duncan Gates", Sys.Date()),
        results = F) 

df %>% distr(popularity, valence) # Some really cool density plots can be done here

df %>% distr(duration_ms)

df %>% 
  filter(duration_ms < 400000) %>%
  distr(duration_ms)

df %>% distr(popularity)

df %>%
  mutate(explicit = as.factor(explicit),
         new_era = ifelse(year %in% c(2000:2020), 1, 0)) %>%
  distr(explicit, new_era)

df %>% 
  mutate(decade = floor(year/10)*10) %>%
  distr(explicit, decade, custom_colours = T, abc = T) 

df %>% distr(mode, force = "char") 

df %>%
  distr(popularity, loudness) + geom_point(color = "yellow")

df %>%
  select(-c(id, name, artists, year, release_date, key)) %>%
  corr_cross(top = 10) # Look at top 10 correlations in the data, key messes with this idk why

table <- df %>%
  select(-c(id, name, artists, year, release_date, key)) %>%
  corr_var(popularity, logs = T, plot = F, top = 10) 
table %>% 
  mutate(variables = paste(toupper(substr(variables, 1, 1)), substr(variables, 2, nchar(as.character(variables))), sep = "")) %>%
  mutate(corr = kableExtra::cell_spec(corr, "html", color = ifelse(corr > 0, "blue", "red")),
         pvalue = kableExtra::cell_spec(pvalue, "html", color = ifelse(pvalue < 0.05, "green", "red"))) %>%
  kableExtra::kable(format = "html", escape = F) %>% 
  kableExtra::kable_styling("striped", full_width = F, position = "center")

# wow OHSE is pretty dope check this out with a better dataframe

df_genre2 %>% distr(genres)

df_genre2 <- df_genre2 %>% na_if("") %>% na.omit %>%
                     mutate(genres = as.factor(genres))

df_genre2 %>% freqs(genres, plot = T,
        title = "Genres by Artist",
        subtitle = paste("Duncan Gates", Sys.Date()),
        results = F)

df_genre2 %>% 
  select(-count) %>%
  summer(genres) %>% 
  mutate_if(is.numeric, funs(round)) %>%
  dplyr::rename(Count = n, `Duration` = duration_ms) %>%
  mutate(`Duration` = paste0(minute(seconds_to_period((`Duration`/(1000*Count)))), 
                                     ":", 
                                     dseconds(round(seconds_to_period((`Duration`/(1000*Count))), digits = 2)))) %>% # Some disgusting lubridate here sorry
  rename_with(str_to_title) %>%
  mutate(Genres = str_to_title(Genres)) %>%
  mutate_at(vars(c("Acousticness", "Danceability", "Energy", "Instrumentalness", "Liveness", "Loudness", "Speechiness", "Tempo", "Valence", "Popularity", "Key", "Mode")), ~round((./Count), digits = 3)) %>% 
  arrange(desc(Count)) %>% 
  reactable(bordered = T,
            highlight = T,
            defaultColDef = colDef(align = "center",
                                   width = 150,
                                   footer = function(values = c("Count", "Acousticness", "Danceability", "Energy", "Instrumentalness", "Liveness", "Loudness", "Speechiness", "Tempo", "Valence", "Popularity", "Key", "Mode")) {
              if (!is.numeric(values)) return()
              sparkline(values, type = "bar", width = 100, height = 30) # Can also do boxplots and line graphs
            }))

make_color_pal <- function(colors, bias = 1) {
  get_color <- colorRamp(colors, bias = bias)
  function(x) rgb(get_color(x), maxColorValue = 255)
} # Make a color function
good_color <- make_color_pal(viridis::magma(n = 12), bias = 2)
good_color2 <- make_color_pal(viridis::viridis(n = 60))
# seq(0.1, 0.9, length.out = 12) %>% 
#   good_color() %>% 
#   scales::show_col() # This just shows the color palette generated

color_table <- df_genre2 %>% 
  select(-count) %>%
  summer(genres) %>% 
  filter(n > 200) %>% # Let's get the top 60 most popular genres
  mutate_if(is.numeric, funs(round)) %>%
  dplyr::rename(Count = n, `Duration` = duration_ms) %>%
  mutate(`Duration` = paste0(minute(seconds_to_period((`Duration`/(1000*Count)))), 
                                     ":", 
                                     dseconds(round(seconds_to_period((`Duration`/(1000*Count))), digits = 2)))) %>% # Some disgusting lubridate here sorry
  rename_with(str_to_title) %>%
  mutate(Genres = str_to_title(Genres)) %>%
  mutate_at(vars(c("Acousticness", "Danceability", "Energy", "Instrumentalness", "Liveness", "Loudness", "Speechiness", "Tempo", "Valence", "Popularity", "Key", "Mode")), ~round((./Count), digits = 3)) %>% 
  arrange(desc(Count))

reactable <- color_table %>% 
  reactable(bordered = T,
            highlight = T,
            columns = list(
              Count = colDef(
                name = "Count",
                style = function(value) {
                  value
                  normalized <- (value - min(color_table$Count)) / (max(color_table$Count) - min(color_table$Count))
                  color <- rev(good_color(normalized))
                  list(background = color)
                }
              ),
              Valence = colDef(
                name = "Valence",
                style = function(value) {
                value
                normalized <- (value - min(color_table$Valence)) / (max(color_table$Valence) - min(color_table$Valence))
                color <- good_color2(normalized)
                list(background = color)
                },
                class = "border"
              )
            ),
            defaultColDef = colDef(align = "center",
                                   width = 150,
                                   footer = function(values = c("Count", "Acousticness", "Danceability", "Energy", "Instrumentalness", "Liveness", "Loudness", "Speechiness", "Tempo", "Valence", "Popularity", "Key", "Mode")) {
              if (!is.numeric(values)) return()
              sparkline(values, type = "box", width = 100, height = 30) # Can also do bar and line graphs
            }))

.table {
  margin: 0 auto;
  width: 675px;
}
.tableTitle {
  margin: 6px 0;
  font-size: 16px;
  font-family: 'Econ Sans Cnd'
}
.tableTitle h2 {
  font-size: 16px;
  font-weight: bold;
  font-family: 'Econ Sans Cnd';
  text-align: center
}
.tableTitle p {
  font-size: 14px;
  font-weight: 500;
  text-align: center
}
.border {
  border-left: 4px solid #555;
  border-right: 4px solid #555;
}

div(class = "tableTitle",
    div(
      class = "title",
      h2("Aggregated Genre Characteristics"),
      p(
        "Each Column is an Average of Genre with Arbitrary Artists"
      ),
    ),
    reactable)

library(ggraph)
library(igraph)
library(graphlayouts)

network_df <- df_genre2 %>% 
  rename_with(str_to_title) %>%
  mutate(Genres = str_to_title(Genres)) %>% 
  relocate(c(Popularity, Count), .after = Artists) %>%
  arrange(desc(Count, Popularity))

corrplot2 <- function(data,
                      method = "pearson",
                      sig.level = 0.05,
                      order = "original",
                      diag = FALSE,
                      type = "upper",
                      tl.srt = 90,
                      number.font = 1,
                      number.cex = 1,
                      mar = c(0, 0, 0, 0)) {
  library(corrplot)
  data_incomplete <- data
  data <- data[complete.cases(data), ]
  mat <- cor(data, method = method)
  cor.mtest <- function(mat, method) {
    mat <- as.matrix(mat)
    n <- ncol(mat)
    p.mat <- matrix(NA, n, n)
    diag(p.mat) <- 0
    for (i in 1:(n - 1)) {
      for (j in (i + 1):n) {
        tmp <- cor.test(mat[, i], mat[, j], method = method)
        p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
      }
    }
    colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
    p.mat
  }
  p.mat <- cor.mtest(data, method = method)
  col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
  corrplot(mat,
    method = "color", col = col(200), number.font = number.font,
    mar = mar, number.cex = number.cex,
    type = type, order = order,
    addCoef.col = "black", # add correlation coefficient
    tl.col = "black", tl.srt = tl.srt, # rotation of text labels
    # combine with significance level
    p.mat = p.mat, sig.level = sig.level, insig = "blank",
    # hide correlation coefficiens on the diagonal
    diag = diag
  )
}

network_df %>% select(-Genres, -Artists) %>% corrplot2()

network_df_filter <- network_df %>% filter(Count > 200) %>% select(Genres, Artists) # Arbitrary filter for artists with more than 100 songs to get an idea of what we're looking at
network_graph <- network_df_filter %>%
  graph_from_data_frame(directed = T) # From igraph
V(network_graph)$size <- degree(network_graph)
ggraph(network_graph, layout = "fr") +
  geom_edge_link(aes(color = Value), edge_color = "grey66", arrow = arrow(type = "closed", length = unit(3, 'mm'))) +
  geom_node_point(aes(size = degree(network_graph), alpha = degree(network_graph)), color = "lightblue", size = 5) +
  geom_node_text(aes(filter = size >= 8, label = name), vjust = 1, hjust = 1, repel = TRUE, family = "serif", fontface = "bold") +
  scale_edge_width(range = c(0.2,3)) +
  scale_size(range = c(1,6)) +
  ggtitle("Mapping Genres and Artists") +
  theme_void() +
  theme(plot.title = element_text(hjust = 0.5),
        legend.position = "none")